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Install Pabot and choose what to parallelize
Pabot is installed as the robotframework-pabot Python package and runs Robot Framework tests as multiple processes on one machine. Install or upgrade it with:
pip install -U robotframework-pabotChoose the split mode based on how your test data is organized:
| Goal | Command | How work is split |
|---|---|---|
| Run independent suite files concurrently | pabot tests |
Suite-level split, Pabot’s default; cases within each suite run sequentially. |
| Run cases from one suite concurrently | pabot --testlevelsplit --processes 2 tests |
Test-level split; the two workers can run separate test cases. |
The Robot Framework guide to running tests in parallel documents --processes as the worker-count option. The documented default is the maximum of two and the CPU count, but that is a capacity default, not a guarantee that the workload or machine can use that many workers effectively.
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Run two test cases from one .robot file in parallel
For cases named, for example, TC001 and TC002 in one suite file, run Pabot against the file or its containing directory and enable test-level splitting:
pabot --testlevelsplit --processes 2 path/to/tests.robotTo run a larger directory with a chosen worker count:
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pabot --testlevelsplit --processes 8 testsHere, --testlevelsplit changes the unit of parallel work from suites to individual test cases; --processes 8 allows up to eight worker processes. Adjust the count to match available CPU, memory, and the capacity of any services or shared systems the tests use. Robot Framework’s normal robot [options] data command executes tests in a suite sequentially; its selection options include --test, --suite, --include, and --exclude. See the Robot Framework User Guide for selection syntax.
Account for setup, teardown, and shared state
Suite setup and teardown can repeat
With test-level splitting, Pabot creates parallel instances of a suite. Suite setup and teardown run for each instance, while test setup and teardown still run for each test case. If suite initialization creates data, allocates a port, or changes an external system, make sure repeated initialization is safe and does not cause instances to overwrite one another. Repeated expensive setup can also reduce the time saved by parallel execution.
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Isolate tests that touch shared resources
Parallel processes may access the same accounts, files, database records, devices, or environment. Give each worker isolated resources where possible. When coordination or resource distribution is needed, PabotLib provides locking and resource distribution; the official guide documents --pabotlib to start it and --resourcefile for resource distribution.
Choose chunking or sharding for different constraints
- Use
--chunkwhen you want to group tests into a limited number of Robot runs. This can let suites share setup and teardown rather than launching a separate run for every test-level work unit. - Use
--shard i/nwhen dividing execution across multiple machines. Sharding is a distribution boundary; it is not the same as increasing the local worker count. - Use PabotLib when tests need coordinated access to shared resources. It addresses coordination, not where the execution is distributed.
These options address different needs. Check the official Pabot documentation for the exact syntax and details of less common options before using them in a production run.
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Troubleshoot parallel runs
- Cases in one suite still run one after another: add
--testlevelsplit. Suite-level splitting is the default. - Pabot is not found after installation: confirm the package was installed in the same Python environment whose scripts are on your shell’s
PATH; retrypip install -U robotframework-pabotin that environment. - Suite initialization happens more than once: this is expected with test-level splitting. Make setup and teardown safe for multiple instances, or consider grouping execution with
--chunkif repeated suite setup is costly. - Parallel tests interfere with each other: isolate their data and external resources, or use PabotLib locking and resource distribution where appropriate.
- More workers do not improve the run: lower
--processesand check whether the tests are limited by CPU, memory, or a shared service. The documented default is not a workload-specific performance recommendation.
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